Development and Validation of Diffuse Idiopathic Pulmonary Neuroendocrine Hyperplasia Diagnostic Criteria

弥漫性特发性肺神经内分泌增生症诊断标准的制定与验证

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Abstract

INTRODUCTION: Diffuse idiopathic pulmonary neuroendocrine hyperplasia (DIPNECH) is a rare condition that is likely underdiagnosed owing to the lack of established and validated diagnostic criteria. These clinical guidelines are empirical and created on the basis of a limited number of studies. This study was designed to validate the existing criteria and to identify new clinical parameters that can accurately diagnose DIPNECH. METHODS: Patients with DIPNECH were identified from a cohort that underwent surgical lung resection for pulmonary carcinoids. The study cohort included a total of 105 consecutive cases with neuroendocrine lesions. Initial diagnostic predictors of DIPNECH were selected from the literature. We employed univariate and multivariate models to evaluate the association of clinical, pathologic, radiologic variables with the likelihood of DIPNECH. RESULTS: Univariate analysis identified age, sex, chronic obstructive pulmonary disease diagnosis, obstructive abnormalities, pulmonary nodules, mosaicism, absolute numbers of pulmonary neuroendocrine lesions (PNELs), and the number of tumorlets as significant DIPNECH predictors (for p < 0.05). After adjustment for sampling variations, the ratio of the total number of PNELs to the number of bronchioles was found to be considerably higher in DIPNECH category. Multivariate analysis identified the total number of PNELs and multiple pulmonary nodules (>10) as independent predictors of DIPNECH. The performance of our criteria revealed an accuracy of 76% in detecting DIPNECH cases. CONCLUSIONS: We proposed a set of diagnostic criteria for DIPNECH on the basis of an expert-panel approach integrating pathological features, radiology, and clinical data. Our findings will help identify DIPNECH patients, without a pathological confirmation of a neuroendocrine lesion. Before the implementation of these criteria in clinical practice, they require further validation in multi-institutional cohorts.

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